Publication Type

Conference Proceeding Article

Publication Date



This paper presents THINC, an agent developed for saving energy in real-world commercial buildings. While previous work has presented techniques for computing energy-efficient schedules, it fails to address two issues, centered on human users, that are essential in real-world agent deployments: (i) incentivizing users for their energy saving activities and (ii) interacting with users to reschedule key “energy-consuming” meetings in a timely fashion, while handling the uncertainty in such interactions. THINC addresses these shortcomings by providing four new major contributions. First, THINC computes fair division of credits from energy savings. For this fair division, THINC provides novel algorithmic advances for efficient computation of Shapley value. Second, THINC includes a novel robust algorithm to optimally reschedule identified key meetings addressing user interaction uncertainty. Third, THINC provides an end-to-end integration within a single agent of energy efficient scheduling, rescheduling and credit allocation. Finally, we deploy THINC in the real-world as a pilot project at one of the main libraries at the University of Southern California and present results illustrating the benefits in saving energy.


Innovative Applications, Energy Conservation, Fair Division, Shapley Value


Artificial Intelligence and Robotics | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics


Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), May, 5–9, 2014, Paris

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Last Page






City or Country

Richland, SC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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